Large-scale traffic grid signal control with regional Reinforcement Learning.

Multi-agent Deep Reinforcement Learning (MADRL) based traffic signal control becomes a popular research topic in recent years. ... To alleviate the scalability issue of completely centralized RL techniques and the non-stationarity issue of completely decentralized RL techniques on large-scale traffic networks, some literature utilizes ... Deep reinforcement learning(DRL) becomes very popular in adaptive traffic signal control(ATSC) due to the huge achievement and success in both reinforcement learning(RL) and deep neural network(DNN). ...

arXiv:2303.11899v5 fatcat:e2ny4kmm25f5tj32yx5y4b3do4

Multiple Versions

Optimizing traffic signal control (TSC) at intersections continues to pose a challenging problem, particularly for large-scale traffic networks. ... This is achieved using graph attention networks (GATs) to serve as the neural network for deep reinforcement learning, which aids in maintaining the graph topology of the traffic network while disregarding ... Prashanth and Bhatnagar proposed reinforcement learning with function approximation for traffic signal control, using Q-learning for adaptive signal control (12) . ...

arXiv:2110.05564v1 fatcat:h6m3xsowo5hcbjmfl2hln3qo6a

We propose the advance decision-making reinforcement learning traffic signal control (AD-RLTSC) algorithm to improve traffic efficiency while ensuring safety in mixed traffic environment. ... Reinforcement learning-based traffic signal control systems (RLTSC) can enhance dynamic adaptability, save vehicle travelling time and promote intersection capacity. ... Third, the impact of the proportion of intelligent traffic signal lights on large-scale traffic networks is explored. ...

doi:10.1093/tse/tdac027 fatcat:4ojitrrpcjbinkzj3cn4w26tri

DOAJ

An intelligent transport system to use the machine learning methods likes reinforcement learning and to explain the acknowledged transportation approaches and a list of recent literature in traffic signal ... In recent years traffic signal control systems have on over simplified information and rule-based methods and we have large amounts of data, more computing power and advanced methods to drive the development ... Deep reinforcement learning agent of traffic signal control [21]. ...

doi:10.32628/cseit206458 fatcat:iyp2r73tqjdq5pqpvxv77mrdra

Open Access

Optimizing traffic signal control (TSC) at intersections continues to pose a challenging problem, particularly for large-scale traffic networks. ... This is achieved using graph attention networks (GATs) to serve as the neural network for deep reinforcement learning. ... Acknowledgments: This work was supported by Purdue University's Center for Connected and Automated Transportation (CCAT), a part of the larger CCAT consortium, a USDOT Region 5 University Transportation ...

doi:10.3390/computers11030038 fatcat:d3zck3x5hvh27i4cqdmx77tnhe

DOAJ

To this end, we propose a Decentralized Reinforcement Learning at the Edge for traffic light control in the IoV (DRLE). ... DRLE decomposes the highly complex problem of large area control. into a decentralized multi-agent problem. We prove its global optima with concrete mathematical reasoning. ... They applied multi-agent reinforcement learning to A2C for large-scale traffic light control in [19] . ...

arXiv:2009.01502v1 fatcat:6zeh7xfyjbfqpeirb3ivlkewru

Open Access Multiple Versions

This paper highlights several properties of large urban networks that can have an impact on machine learning methods applied to traffic signal control. ... Networks with different parameters exhibit very different responses to traffic signal control. ... APPENDIX A THE TRAINING ALGORITHM REINFORCE-TD ...

arXiv:2008.10989v2 fatcat:q6itymizc5edplbt7p7ejontfa

Multiple Versions

of traffic signal control. ... To the best of our knowledge, we are the first to use graph attentional network in the setting of reinforcement learning for traffic signal control. ... CONCLUSION In this paper, we propose a well-designed reinforcement learning approach to solve the network-level traffic light control problem. ...

arXiv:1905.05717v1 fatcat:vub6ajsevnhg5poz4l2z4gkttq

Multiple Versions

This paper uses supervised learning, random search and deep reinforcement learning (DRL) methods to control large signalized intersection networks. ... We find that: (i) a policy trained with supervised learning with only two examples outperforms LQF, (ii) random search is able to generate near-optimal policies, (iii) the prevailing average network occupancy ... In the case of traffic signal control for large-scale grid network, methods based on transition probabilities are impractical because the state-action space tends to be too large as the number of agents ...

arXiv:1908.02673v1 fatcat:ngeffbvo2jcgbebfnbhi5zizbq

Those types of congestions cannot be adequately resolved by the traditional Adaptive Traffic Signal Control (ATSC). ... The Deep Reinforcement Learning (DRL) framework uses Deep Neural Networks (DNN) to digest raw traffic data to approximate the quality function of RL. ... The main idea in this approach is to tackle large-scale control problems by transferring learned base models into the sub-regional grids. ...

doi:10.3390/app10114011 fatcat:vnhrunfhpbgtrmky7pcvdg223a

DOAJ

Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning power ... The proposed multi-agent A2C is compared against independent A2C and independent Q-learning algorithms, in both a large synthetic traffic grid and a large real-world traffic network of Monaco city, under ... MA2C is evaluated in both a synthetic large traffic grid and a real-world large traffic network, with delicately designed traffic dynamics for ensuring a certain difficulty level of MDP. ...

arXiv:1903.04527v1 fatcat:hmzh7562dvc4jinmc3exahhpgu

INDEX TERMS Urban network traffic control, distributed traffic signal control system, multi-agent reinforcement learning, mixed strategy Nash-equilibrium, numerical simulation. ... This paper designs a distributed control method for preventing disturbancebased urban network traffic congestion by integrating Multi-Agent Reinforcement Learning (MARL) and regional Mixed Strategy Nash-Equilibrium ... In Section II, we present a literature review with focus on various control methods, multi-agent systems and multiagent reinforcement learning applied to traffic signal control. ...

doi:10.1109/access.2020.2968937 fatcat:3d57ii6wnzgjpdg72zcgkza764

DOAJ

Multi-agent reinforcement learning (MARL) has been applied and shown great potential in multi-intersections traffic signal control, where multiple agents, one for each intersection, must cooperate together ... To encourage global cooperation, previous work partitions the traffic network into several regions and learns policies for agents in a feudal structure. ... Experiments To evaluate the performance of our method, we compared with several conventional and state-of-the-art RL methods for traffic signal control in a synthetic traffic grid and three real-world ...

arXiv:2205.13836v1 fatcat:7dj2yof3xzeh5hpb4akyvmo7eu

deep Q-network and deep deterministic policy gradient reinforcement learning controllers. ... Developing optimal transportation control systems at the appropriate scale can be difficult as cities' transportation systems can be large, complex and stochastic. ... Q-learning N/A [28] Toronto, Canada 59 Game Theory Q-learning Tabular [47] N/A 50 Holonic Q-learning N/A [48] Grid 22 Reward Sharing Q-learning Bayesian [49] Grid 100 Regional ...

arXiv:1909.00395v1 fatcat:6dihqugg5jalvdcyrkpmtltvmy

In the adaptive traffic signal control (ATSC), reinforcement learning (RL) is a frontier research hotspot, combined with deep neural networks to further enhance its learning ability. ... in the entire large-scale transportation network. ... For large-scale control tasks through DRL, the work in [25] considers the application of policy gradient methods to control traffic signal timing. ...

doi:10.1155/2021/6693636 fatcat:7amnnzidtjbhnlnydlzfiiydg4

DOAJ

Large-Scale Traffic Signal Control Using Constrained Network Partition and Adaptive Deep Reinforcement Learning [article]

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Scalable Traffic Signal Controls using Fog-Cloud Based Multiagent Reinforcement Learning [article]

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Traffic signal control in mixed traffic environment based on advance decision and reinforcement learning

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A Survey on Deep Reinforcement Learning Network for Traffic Light Cycle Control

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Scalable Traffic Signal Controls Using Fog-Cloud Based Multiagent Reinforcement Learning

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DRLE: Decentralized Reinforcement Learning at the Edge for Traffic Light Control [article]

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Congested Urban Networks Tend to Be Insensitive to Signal Settings: Implications for Learning-Based Control [article]

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CoLight: Learning Network-level Cooperation for Traffic Signal Control [article]

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Large-scale traffic signal control using machine learning: some traffic flow considerations [article]

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Application of Deep Reinforcement Learning in Traffic Signal Control: An Overview and Impact of Open Traffic Data

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Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control [article]

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A distributed control method for urban networks using Multi-Agent Reinforcement Learning based on regional Mixed Strategy Nash-Equilibrium

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Feudal Multi-Agent Reinforcement Learning with Adaptive Network Partition for Traffic Signal Control [article]

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An Open-Source Framework for Adaptive Traffic Signal Control [article]

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Coordinated Control of Distributed Traffic Signal Based on Multiagent Cooperative Game

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